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We study the single class SVM (SCSVM) classifier performance on the positive data points while considering the impact of SCSVM on negative protein pair data points. We compare the result with the AA classifier (amino acids maximum entropy classifier)  to see if a better performance can be achieved for the same data configuration. The conclusion is that although positive classifier is slightly better than the negative one, the SCSVM classifier performance does not outperform the AA classifier for current data configuration. The "vote" strategy does not change the SCSVM's ROC behavior but increase the confidence of the true positive. Our explanation is that in SCSVM, only one class of training data is available. It is very hard to determine how tight the decision boundary should be to best characterize the known class. Due to the same reason, SCSVM tends to over-fit and under-fit easily. Furthermore, the SCSVM's performance depends on testing data's distribution.